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全部共 54 篇
AI 靶点发现临床研究International journal of molecular sciences

A Mechanistic Map of the Phenotypic Complexity in Recessive Dystrophic Epidermolysis Bullosa Provides Basis for Therapeutic Target Discovery and Drug Repurposing.

0 次引用趋势数据不足

隐性营养不良型大疱性表皮松解症(RDEB)由COL7A1基因功能缺失突变所致,表型复杂异质,临床可见皮肤脆性增加、局部及全身炎症、伤口愈合障碍、菌群失调、疼痛和瘙痒,并增加皮肤癌风险。本研究旨在构建RDEB分子机制的可操作模型,以识别并优选药物重定位的治疗靶点。研究利用儿童RDEB患者与未受累个体皮肤活检的转录组数据构建疾病机制图谱,并结合机器学习从图谱回路中推断可操作靶点、识别可逆转转录组改变的候选药物。所得图谱包含13条改变信号回路和10个效应蛋白,与炎症、纤维化及肿瘤进展等疾病特征相关;分析识别出66个可操作靶点及74个候选药物,富集分析显示磷酸二酯酶类及抗肿瘤和免疫调节剂富集。最终优先选出16种可逆转回路改变且不增强非预期效应的药物,其中苄氟噻嗪和伊布利特影响最大。该机制模型提供了首个RDEB功能性疾病图谱,并揭示了由已批准药物靶向的有前景的治疗候选物。

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AI 辅助短评,基于标题和摘要整理

本研究针对RDEB表型高度异质、缺乏可操作分子模型的问题,基于患儿皮肤转录组构建首個功能性机制图谱,结合机器学习推断靶点并重定位药物。其价值在于将疾病回路与炎症、纤维化、肿瘤进展等标志关联,为靶点发现和已批准药物重定位提供系统依据。

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完整作者

Sara Herráiz-Gil、Miriam Payá-Milans、María Del Carmen de Arriba、Lucía Martínez-Santamaría、Carlos Loucera、Nuria Illera、Marcela Del Río、Joaquín Dopazo、Carlos León、María José Escámez

作者机构

  • Departamento de Bioingeniería, Universidad Carlos III de Madrid, ROR Code 03ths8210, Avenida de la Universidad, 30 (Edificio Sabatini), 28911 Leganés, Spain.
  • Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), 28029 Madrid, Spain.
  • Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD, UAM), 28040 Madrid, Spain.
  • Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, 28040 Madrid, Spain.
  • Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, 41092 Seville, Spain.
  • Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, 41013 Seville, Spain.

技术路线

RDEB患儿与未受累个体皮肤活检转录组数据 → 构建由13条改变信号回路和10个效应蛋白组成的机制图谱并结合机器学习推断可干预靶点、识别可逆转转录组改变的候选药物

最近 5 个完整年度引用趋势

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不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/30 03:55
临床队列临床研究Cancer cell

Chemotherapy-free phase 1/2 trial of HRS-4642 plus adebrelimab in previously treated metastatic KRAS G12D-mutant pancreatic cancer.

1 次引用趋势数据不足

一项1/2期试验(NCT06427239)评估选择性KRAS G12D抑制剂HRS-4642联合PD-L1阻断剂adebrelimab,用于48例既往接受过治疗的转移性KRAS G12D突变胰腺导管腺癌患者。剂量递增未出现剂量限制性毒性,确定了2期推荐剂量,未发生治疗相关死亡;最常见治疗相关不良事件为高胆固醇血症和贫血(各50%)。在2期推荐剂量队列(n=37)中,确认的客观缓解率为43.2%(95%CI 27.1-60.5),疾病控制率为81.1%(95%CI 64.8-92.0),中位缓解持续时间为6.9个月(95%CI 4.8-8.6),中位无进展生存期为5.7个月(95%CI 4.0-7.5),中位总生存期为11.5个月(95%CI 9.2-16.9)。该无化疗方案耐受性良好,在难治性KRAS G12D突变PDAC中显示出令人鼓舞的抗肿瘤活性,值得进一步研究。

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编辑短评

经治 KRAS G12D 突变转移性胰腺癌后线治疗选择匮乏;该 Ⅰ/Ⅱ 期研究证实 HRS‑4642 联合 adebrelimab 的无化疗方案安全性可控、应答率高,为这类患者提供去化疗的靶向免疫联合新方向。

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完整作者

Si Shi、Miaoyan Wei、Nan Du、Jialin Li、Boyue Han、Wei Wang、Xin Hu、Fei Luo、Jin Xu、Xianjun Yu

作者机构

  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China. Electronic address: shisi@fudanpci.org.
  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China.
  • Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Clinical Research & Development, Jiangsu Hengrui Pharmaceuticals Co., Ltd, Shanghai, China.
  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China. Electronic address: xujin@fudanpci.org.
  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China. Electronic address: yuxianjun@fudanpci.org.

技术路线

48例经治转移性KRAS G12D突变PDAC患者入组→剂量递增确定RP2D并汇总治疗相关不良事件→RP2D队列(n=37)疗效分析(确认ORR、DCR、DoR、PFS、OS及95%CI)

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:52
临床队列临床研究Nature medicine

A clinically-oriented foundation model for intraoperative pathology.

0 次引用趋势数据不足

研究基于十家医疗中心逾10万张冰冻切片构建了面向临床的术中病理基础模型CRISP,并在近100项回顾性诊断任务、逾1.5万张术中切片上完成系统评估,涵盖良恶性判别、术中关键决策及泛癌检测等。模型在6家机构、14种肿瘤类型和24个解剖部位(含既往未见部位及罕见癌)表现出稳健泛化能力。在逾3000例患者的前瞻性队列中,CRISP在真实条件下保持较高诊断准确性,直接为92.6%病例的手术决策提供依据;人机协作使诊断工作量减少35%,避免105项辅助检查,并将微转移灶检出准确率提升至87.5%。研究提示,CRISP为AI驱动的术中病理提供了面向临床的路径,有望支持手术决策并促进计算方法向临床实践转化。

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编辑短评

通用病理基础模型多基于石蜡切片,难以适配冰冻切片伪影与术中场景;CRISP 以大规模冰冻切片预训练,经多中心前瞻性验证,可跨器官、跨肿瘤类型辅助术中病理判读,减轻病理医师负担、提升微转移检出能力,支撑外科实时决策。

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完整作者

Zihan Zhao、Fengtao Zhou、Ronggang Li、Bing Chu、Xinke Zhang、Xueyi Zheng、Ke Zheng、Xiaobo Wen、Jiabo Ma、Yihui Wang、Jiewei Chen、Chengyou Zheng、Jiangyu Zhang、Yongqin Wen、Jiajia Meng、Ziqi Zeng、Xiaoqing Li、Jing Li、Dan Xie、Yaping Ye、Yu Wang、Hao Chen、Muyan Cai

作者机构

  • Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Department of Gastric Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China.
  • Department of Pathology, Jiangmen Central Hospital, Jiangmen, China.
  • Department of Pathology, Zhongshan People's Hospital, Zhongshan, China.
  • Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
  • Department of Pathology, The Tenth Affiliated Hospital, Southern Medical University, Dongguan People's Hospital, Dongguan, China.
  • Department of Pathology, Huizhou Central People's Hospital, Huizhou, China.
  • Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
  • Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China. jhc@ust.hk.
  • Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China. jhc@ust.hk.
  • Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China. jhc@ust.hk.
  • HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China. jhc@ust.hk.
  • State Key Laboratory of Nervous System Disorders, The Hong Kong University of Science and Technology, Hong Kong, China. jhc@ust.hk.
  • SIAT-HKUST Joint Laboratory for Brain Science, The Hong Kong University of Science and Technology, Hong Kong, China. jhc@ust.hk.
  • Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China. caimy@sysucc.org.cn.

技术路线

数据/样本:来自10个医疗中心的逾10万张冰冻切片构建CRISP基础模型;分析:在近100项回顾性诊断任务、逾1.5万张术中切片上评估,并观察跨6家机构、14种肿瘤类型、24个解剖部位的泛化性;验证:3000余例患者的前瞻性队列验证,评估真实条件下诊断准确性、对手术决策的影响及人机协作效果

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:53
公共数据库临床研究Nature medicine

Phenomic profiles and disease patterns of 1.9 million participants from Our Future Health.

1 次引用趋势数据不足

“我们的未来健康”是一项旨在招募500万英国成年居民的前瞻性研究,以推动疾病预防、检测和治疗方法的发现与转化。目前已入组超过250万人,逾190万参与者具备基线表型数据。研究评估了自报健康相关行为、地理位置、诊断与用药、门急诊就诊、癌症登记及死因等表型,并将疾病模式与国家估计值及英国生物样本库队列进行比较。社会人口学、生活方式和健康相关特征总体反映英国人群模式,但除一个少数族裔群体外,其他少数族裔及社会经济最 deprived 群体代表性不足。多种主要自报疾病患病率高于国家估计值,其中心理健康问题(如抑郁和焦虑)尤为突出,且与英国生物样本库方向一致(r=0.78);与已知临床相关因素的关联在两个队列中均得到重复(r=0.80)。用药模式和癌症患病率呈现预期年龄梯度,肺癌率低于国家数据。随着招募推进,电子健康记录有助于明确疾病模式并系统评估偏倚。

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完整作者

Vincent J Straub、Stefania Benonisdottir、Giovanni Scotti Bentivoglio、Neil Wary、Robert Campbell、Augustine Kong、Melinda C Mills

作者机构

  • Leverhulme Centre for Demographic Science, Nuffield Department of Population Health, University of Oxford and Nuffield College, Oxford, UK. vincent.straub@ndph.ox.ac.uk.
  • Leverhulme Centre for Demographic Science, Nuffield Department of Population Health, University of Oxford and Nuffield College, Oxford, UK.
  • Institute of Physical Sciences, University of Iceland, Reykjavík, Iceland.
  • Department of Economics, Econometrics and Finance, University of Groningen, Groningen, the Netherlands.
  • Leverhulme Centre for Demographic Science, Nuffield Department of Population Health, University of Oxford and Nuffield College, Oxford, UK. melinda.mills@demography.ox.ac.uk.
  • Department of Economics, Econometrics and Finance, University of Groningen, Groningen, the Netherlands. melinda.mills@demography.ox.ac.uk.
  • Department of Genetics, University Medical Centre Groningen, Groningen, the Netherlands. melinda.mills@demography.ox.ac.uk.

技术路线

数据/样本:Our Future Health前瞻性研究中>190万参与者的基线表型数据(自报健康行为、地理位置、诊断与用药、住院/门诊记录、癌症登记及死因) → 分析:描述性统计并与英国国家估计及UK Biobank队列比较疾病模式、计算相关性

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/30 06:05
临床队列综述Briefings in bioinformatics

Advances in computational biology approaches for digital modeling of tumor microenvironment.

0 次引用趋势数据不足

肿瘤微环境(TME)是异质性多尺度系统,仅靠实验难以完整刻画。计算建模可整合细胞、血管、代谢、免疫与力学机制,但方法多样,给模型选择、参数化、验证和临床转化带来困难。本文以问题为导向综述TME建模的计算方法,比较连续、离散及离散-连续混合范式在生物学分辨率、数据需求、可解释性和计算成本上的差异,并将其关联至肿瘤细胞动力学、血管运输、细胞外基质与力学、代谢与缺氧、免疫互作五个机制领域;从参数可识别性、不确定性量化、数值有效性、生物学保真度和临床相关性考察模型可信度,强调按预期用途和验证情境解读性能。文章还分析机器学习在机制-ML整合中的功能,包括代理模型加速前向模拟、反演方法估计潜参数与状态、患者特异性工作流整合多模态临床数据与机制模型,以及物理信息神经网络嵌入控制方程;并讨论纵向个性化、物理感知多尺度整合、临床可部署数字孪生、外部泛化和合成数据到临床迁移,提出从机制假设生成到自适应数字孪生决策支持的转化就绪路径。可信的TME建模应优先采用最小充分模型,明确预期用途,透明呈现不确定性,并使证据与生物学或临床问题相匹配。

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最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:57
公共数据库临床研究World journal of surgical oncology

Association of different adiposity indices with breast cancer incidence and all-cause mortality: a nationwide cohort study.

0 次引用趋势数据不足

美国一项基于国家健康与营养调查(NHANES)2005—2016年周期数据的全国性队列研究,纳入12,829名20~85岁女性,评估相对脂肪量(RFM)、腰高比(WHtR)、体型指数(ABSI)、倒数体重指数(RPI)和锥度指数(CI)5项肥胖指标与乳腺癌发病及全因死亡的关系。中位随访101.4个月,期间发生367例乳腺癌、1,132例死亡。多因素校正后,ABSI与乳腺癌发病(OR=1.21,95%CI:1.02~1.44)及全因死亡(HR=1.22,95%CI:1.12~1.34)均呈显著正相关,在≥50岁女性中关联更强;CI亦与死亡正相关(HR=1.12);RFM、WHtR和RPI在≥50岁亚组中与乳腺癌呈负相关。ABSI对乳腺癌预测的AUC最高(0.606),剂量反应关系最一致。结论认为,5项肥胖指标中ABSI与乳腺癌发病和全因死亡的关联最强且最一致,在各年龄组中预测价值稳定,未来仍需更大规模前瞻性队列研究进一步探讨。

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最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:24
公共数据库基础研究Journal of translational medicine

Novel androgen receptor N-terminal mutation drives gastric cancer metastasis via YWHAZ-mediated FAK pathway activation.

0 次引用趋势数据不足

研究对象为胃癌及AR N端A32C(p.Y11S)突变。研究分析TCGA数据库33种肿瘤并在65例胃癌中验证,采用增殖、凋亡、迁移实验及转录组分析、免疫共沉淀、FAE226药物抑制和肝转移模型研究机制。结果显示,与野生型AR相比,A32C突变显著增强增殖、抗凋亡能力及G1/S期转换;Y11S位点替换增强AR与YWHAZ结合,形成细胞质支架,自发激活FAK-PXN轴及下游PI3K-AKT/ERK通路;敲低YWHAZ或抑制FAK可逆转恶性表型。体内实验中,MT-AR细胞肝脏定植能力高于野生型细胞,转移灶中FAK活性持续较高。结论认为,AR A32C突变是此前未被识别的致癌驱动因素,通过YWHAZ介导的配体非依赖性FAK激活机制促进胃癌转移,拓展了AR突变生物学研究范围,并提示基于AR突变状态的FAK抑制剂可作为AR突变胃癌的精准治疗。

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最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:24
公共数据库基础研究Journal of translational medicine

Chromogranin B promotes EMT-associated stem-like properties and chemoresistance in HNSCC.

0 次引用趋势数据不足

头颈部鳞状细胞癌(HNSCC)中,上皮-间质转化(EMT)相关干性与治疗耐药是推动疾病进展的重要因素,但其上游调控因子尚不明确。研究整合TCGA-HNSC、GEO及单细胞RNA测序数据,结合EMT评分、基因集富集分析、加权基因共表达网络分析和Cox回归,评估嗜铬粒蛋白B(CHGB)的表达、预后价值及其与EMT的关联,并通过功能获得/缺失、异种移植、肿瘤球形成、顺铂敏感性及挽救实验探究其生物学功能与机制。结果显示,CHGB在HNSCC组织中显著上调并与总生存期不良相关,其表达与EMT相关转录程序密切相关。敲低CHGB可抑制增殖、迁移、细胞周期进展、原代及继代肿瘤球形成、体外顺铂耐药和体内肿瘤生长,过表达则呈相反效应。机制上,CHGB通过PI3K/AKT信号维持ZEB1表达,促进EMT相关干性和化疗耐药;恢复ZEB1可部分挽救CHGB敲低所致的自我更新能力受损和顺铂敏感性增强,而沉默ZEB1可减弱CHGB驱动的恶性表型。单细胞转录组分析进一步证实CHGB富集于恶性细胞,并与EMT和干性程序正相关。结论认为,CHGB通过PI3K/AKT/ZEB1轴调控EMT相关干性和化疗耐药,是HNSCC潜在的预后生物标志物和治疗靶点。

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暂未整理

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完整作者

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作者机构

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技术路线

暂未整理

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 02:32
合成生物学综述Molecular cancer

Trace elements and tumors: homeostatic networks, molecular mechanisms and new horizons for precision intervention.

0 次引用趋势数据不足

痕量元素铁、铜、锌、硒、锰是生命必需物质,作为酶辅因子、结构组分和信号分子参与能量代谢、氧化还原平衡及基因组稳定性调控,其生理功能呈剂量-效应双相性,稳态失衡既可维护健康也可促进恶性转化。本综述系统阐述上述五种核心痕量元素在生理条件下的完整生命周期及其功能互联,并阐释肿瘤细胞通过元素成瘾机制劫持该调控网络。文章详述铁依赖性铁死亡与铜依赖性铜死亡的新兴作用,以及痕量元素对凋亡和自噬的情境依赖性调控,揭示肿瘤特异性代谢脆弱性。在肿瘤免疫微环境中,锌和硒通常支持抗肿瘤免疫,铁和铜失调驱动免疫抑制极化,锰则为具免疫治疗潜力的cGAS-STING激动剂。在血管生成与转移方面,铜和铁在细胞外基质重塑、上皮-间质转化及血管生成信号中发挥核心作用。临床证据表明,全身及局部元素失衡可作为多种癌症的风险生物标志物。治疗策略包括传统营养与药理干预、基于元素代谢的放化疗及免疫治疗协同增敏,以及元素响应纳米平台和合成生物学工程免疫细胞等交叉工程方法。多数治疗证据来自临床前模型,需严谨基础研究支撑临床转化。

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AI 辅助短评,基于标题和摘要整理

本文系统梳理铁、铜、锌、硒、锰的稳态网络与肿瘤代谢劫持机制,涵盖铁死亡、铜死亡及免疫微环境调控,并整合元素失衡作为风险标志物的临床证据。治疗策略分类清晰,但作者明确多数证据源于临床前模型,转化前需严谨基础研究。综述框架具系统生物学视角,知识缺口梳理有助于假设驱动探索,适合作为该领域参考。

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完整作者

Shengxin Zhang、Keqin Tan、Qin Zhang、Yuzhe Zhao、Ziyang Cheng、Xinggang Yang、Jingsi Dong、Jiaming Liu、Xuelei Ma

作者机构

  • Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Department of Postgraduate Students, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
  • Department of Hematology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
  • Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
  • Lung Cancer Center/Lung Cancer Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China. dongjingsi@wchscu.edu.cn.
  • Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China. dongjingsi@wchscu.edu.cn.
  • Department of Urology, West China Hospital, Sichuan University, Chengdu, China. JM3099@163.com.
  • Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China. maxuelei0726@wchscu.cn.

技术路线

文献综述:整合基础与临床数据 → 归纳五种微量元素(铁、铜、锌、硒、锰)的稳态网络、分子机制及肿瘤特异性代谢脆弱性

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 03:49
公共数据库临床研究Cell

Advancing cancer detection and treatment using longitudinal routine clinical data.

2 次引用趋势数据不足

肿瘤管理在诊疗连续过程中仍较分散,从晚期诊断、挽救治疗到非个体化监测均存在碎片化问题。为此,研究提出统一多模态Transformer模型Oncoformer,基于中国肿瘤多模态预测与监测研究(COMPASS)队列(367万人、1770万次就诊)训练,并在包括英国生物样本库在内的独立外部队列中验证。该模型整合纵向电子健康记录与胸部X线影像,可完成泛癌诊断(AUROC=0.956)、诊断前1年内未来癌症预测(AUROC=0.869)、肿瘤分期推断(平均AUROC>0.90)、患者特异性治疗反应预测及10种癌症的无复发生存分层(均p<0.01)。分期预测经术后病理终点独立验证,并显示集中于核心癌症基因组通路。Oncoformer通过将常规临床数据转化为癌症动态演化视图,为基于常规临床数据的风险知情癌症预测与治疗分层提供了框架。

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编辑短评

癌症是动态演化疾病,碎片化、单任务模型不匹配临床需求;Oncoformer 证明整合纵向常规临床 + 影像时序信息,能用一套模型实现提前预警、诊断、分期、疗效与复发风险分层,把静态筛查升级成动态个体化肿瘤全程监测。

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完整作者

Fei Liu、Kai Wang、Hui Xu、Cheng Tang、Xian Shen、Meihao Wang、Lei Yang、Li Yang、Li Liu、Changxi Hu、Gen Li、Wei Wu、Zixing Zou、Bingzhou Li、Sian Liu、Jin Kang、Jungho Kong、Ting Li、Io Nam Wong、Xiaoying Huang、Gang Chen、Wenyang Lu、Ian Ziyar、Charlotte L Zhang、Yiwen Sun、Weihong Lin、Caiwen Ou、Manson Fok、Taiwa Hou、Winston Wang、Kanmin Xue、Yun Yin、Hao Zhu、Jonathan Gootenberg、Omar O Abudayyeh、Michael Karin、Alexandre Loupy、John E J Rasko、Trey Ideker、Huiyan Luo、Eric Oermann、Kang Zhang、International Consortium of Digital Twins in Healthcare and Medicine

作者机构

  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Department of Urology, State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Guangzhou National Laboratory, Guangzhou, China.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; State Key Laboratory of Eye Health, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Eye Health, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Department of General Surgery, Department of Hepatobiliary Surgery, Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Department of Radiology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Key Laboratory of Intelligent Medical Imaging of Wenzhou, Healthcare Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Department of Orthopedics, Key Laboratory of Hepatosplenic Surgery of Ministry of Education, the First Affiliated Hospital, Harbin Medical University, Harbin, China.
  • Department of Respiratory and Critical Care Medicine, Center for Big Data and Intelligent Medicine, the First Affiliated Hospital of Chongqing Medical University, Key Laboratory of Digital Health and Intelligent Medicine, Chongqing Municipal Health Commission, Chongqing Translational Medicine Center, Chongqing, China.
  • Department of Health Management, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Peking-Tsinghua Center for Life Sciences, Beijing, China.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Guangzhou National Laboratory, Guangzhou, China.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China.
  • Departments of Medicine and Bioengineering, Moores Cancer Center, School of Medicine, UC San Diego, CA, USA.
  • Division of Pulmonary Medicine, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou Key Laboratory of Interdisciplinary and Translational Medicine, Wenzhou Key Laboratory of Heart and Lung, Wenzhou, Zhejiang, China.
  • Nepean Hospital, Sydney, Australia.
  • Key Laboratory of Intelligent Medical Imaging of Wenzhou, Healthcare Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Dongguan Hospital, Southern Medical University, Guangzhou, China.
  • Conde S. Januario Hospital, Macau, China.
  • Department of Internal Medicine, Mayo Clinic, Arizona, USA.
  • Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Faculty of Health and Wellness, Faculty of Business, City University of Macau, Taipa, Macau SAR, China.
  • Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Gene and Cell Therapy Institute, Mass General Brigham, Cambridge, MA, USA.
  • Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015 Paris, France; Department of Kidney Transplantation, Assistance Publique - Hôpitaux de Paris, Paris, France.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China.
  • Departments of Medicine and Bioengineering, Moores Cancer Center, School of Medicine, UC San Diego, CA, USA; Big Data Institute, Oxford University, Oxford, UK.
  • Department of Medical Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China.
  • Departments of Neurosurgery, Radiology, and Data Science, Neuroscience Institute, NYU Langone Medical Center, New York University, New York, NY, USA.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Guangzhou National Laboratory, Guangzhou, China; State Key Laboratory of Eye Health, Eye Hospital, Wenzhou Medical University, Wenzhou, China. Electronic address: kang.zhang@gmail.com.

技术路线

3.67 million individuals/17.7 million clinical visits (COMPASS cohort, longitudinal EHR + chest X-ray) → Oncoformer unified multimodal transformer analysis → validation on independent external cohorts including UK Biobank, with staging independently validated against postoperative pathological endpoints and convergence on core cancer genomic pathways

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 06:32
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